2020 excess mortality & voting patterns in CH
Redistributed cantonal deaths
Data
Spatial
kt = read_rds("data/BfS/kt.Rds")
gg = read_rds("data/BfS/gg.Rds")
tg3o = read_rds("data/BfS/tg3o.Rds")
se_alt = read_rds("data/BfS/se_alt.Rds")Downscaled data
exp_deaths_2020_year_gem = read_rds("results/exp_deaths_2020_year_gem.Rds") %>%
select(-munici_excess_rat)Excess deaths per 1000 pop
Distribution
Maps
x <categorical>
# total N=2141 valid N=2141 mean=3.00 sd=1.41
Value | N | Raw % | Valid % | Cum. %
---------------------------------------------
[0.00, 1.49) | 429 | 20.04 | 20.04 | 20.04
[1.49, 2.08) | 428 | 19.99 | 19.99 | 40.03
[2.08, 2.64) | 428 | 19.99 | 19.99 | 60.02
[2.64, 3.52) | 428 | 19.99 | 19.99 | 80.01
[3.52,27.78] | 428 | 19.99 | 19.99 | 100.00
<NA> | 0 | 0.00 | <NA> | <NA>
Choropleth
Proportional symbols
Symbol size perceptually scaled to population size.
EDA June vote
Map
Correlations
Unweighted
cor.test(exp_deaths_2020_year_gem$munici_excess_pop,
exp_deaths_2020_year_gem$vote_yes_jun_perc,
method = "pearson")
Pearson's product-moment correlation
data: exp_deaths_2020_year_gem$munici_excess_pop and exp_deaths_2020_year_gem$vote_yes_jun_perc
t = 0.63465, df = 2139, p-value = 0.5257
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.02865836 0.05605123
sample estimates:
cor
0.01372105
Weighted
wtd.cor(exp_deaths_2020_year_gem$munici_excess_pop,
exp_deaths_2020_year_gem$vote_yes_jun_perc,
weight = exp_deaths_2020_year_gem$munici_observed) correlation std.err t.value p.value
Y 0.005492431 0.02188932 0.2509183 0.8019019
Scatter
Unweighted
Weighted
Box
EDA Nov vote
Map
Correlations
Unweighted
cor.test(exp_deaths_2020_year_gem$munici_excess_pop,
exp_deaths_2020_year_gem$vote_yes_nov_perc,
method = "pearson")
Pearson's product-moment correlation
data: exp_deaths_2020_year_gem$munici_excess_pop and exp_deaths_2020_year_gem$vote_yes_nov_perc
t = -3.6713, df = 2139, p-value = 0.0002473
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.12108777 -0.03689184
sample estimates:
cor
-0.07913093
Weighted
wtd.cor(exp_deaths_2020_year_gem$munici_excess_pop,
exp_deaths_2020_year_gem$vote_yes_nov_perc,
weight = exp_deaths_2020_year_gem$munici_observed) correlation std.err t.value p.value
Y -0.130383 0.02170279 -6.007662 0.000000002214821